Improving the accuracy of Website Phishing Detection through Spotted Hyena Optimization Algorithm and Artificial Neural Network

One of the major challenges in cyber space is the existence of fake or phishing pages that needs the attention of command control systems. In phishing attacks, people are directed to fake pages and their important information is stolen by a thief or phisher. Machine learning and data mining algorithms are the widely used algorithms for phishing websites classification. Feature selection has a great influence on the classification results. In this research, an improved spotted Hyena optimization algorithm (ISHOA) is proposed to select appropriate features for classifying phishing websites through artificial neural network. The proposed ISHOA outperformed the standard spotted Hyena optimization algorithm with 98.64% better accuracy. In addition, the results indicate the superiority of ISHOA to three other meta-heuristic algorithms including: particle swarm optimization, firefly algorithm, and bat algorithm. The proposed algorithm is also compared with a number of classification algorithms proposed before on the same dataset and its dominance is showed.